Search results for: click prediction

Authors: Helene Thieblemont, Fariborz Haghighat

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Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control.

Keywords: anticipatory control, model predictive control, solar radiation forecast, thermal storage

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Authors: Vera Wilden, Benno Hoffmeister, Georgios Balaskas, Lukas Rauber, Burkhard Walter

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Light-weight timber frame elements represent an efficient structural solution for wooden multistory buildings. The wall elements of such buildings – which act as shear diaphragms- provide lateral stiffness and resistance to wind and seismic loads. The tendency towards multi-story structures leads to challenges regarding the prediction of stiffness, strength and ductility of the buildings. Lightweight timber frame elements are built up of several structural parts (sheeting, fasteners, frame, support and anchorages); each of them contributing to the dynamic response of the structure. This contribution describes the experimental and numerical investigation and development of enhanced lightweight timber frame buildings. These developments comprise high-performance timber frame walls with the variable arrangements of sheathing planes and dissipative anchors at the base of the timber buildings, which reduce damages to the timber structure and can be exchanged after significant earthquakes. In order to prove the performance of the developed elements in the context of a real building a full-scale two-story building core was designed and erected in the laboratory and tested experimentally for its seismic performance. The results of the tests and a comparison of the test results to the predicted behavior are presented. Observation during the test also reveals some aspects of the design and details which need to consider in the application of the timber walls in the context of the complete building.

Keywords: dissipative anchoring, full scale test, push-over-test, wood shear walls

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Authors: Orose Rugchati, Khumthong Mahawongwiriya

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Dendrobium hybrid cv. Ear-Sakul has become one of the important commercial commodities in Thailand agricultural industry worldwide, either as potted plants or as cut flowers due to the attractive color produced in flower petals. Anthocyanins are the main flower pigments and responsible for the natural attractive display of petal colors. These pigments play an important role in functionality, such as to attract animal pollinators, classification, and grading of these orchids. Dendrobium hybrid cv. Ear-Sakul has been collected from local area farm in different stage flowers (F1, F2-F5, and F6). Anthocyanins pigment were extracted from the fresh flower by solvent extraction (MeOH–TFA 99.5:0.5v/v at 4ºC) and purification with ethyl acetate. The main anthocyanins components are cyanidin, pelargonidin, and delphinidin. Pure anthocyanin contents were analysis by UV-Visible spectroscopy technique at λ max 535, 520 and 546 nm respectively. The anthocyanins contents were converted in term of monomeric anthocyanins pigment (mg/L). The anthocyanins contents of all sample were compared with standard pigments cyanidin, pelargonidin and delphinidin. From this experiment is a simple extraction and analysis anthocyanins content in different stage of flowers results shown that monomeric anthocyanins pigment contents of different stage flowers (F1, F2-F5 and F6 ): cyanidin – 3 – glucoside (mg/l) are 0.85+0.08, 24.22+0.12 and 62.12+0.6; Pelargonidin 3,5-di- glucoside(mg/l) 10.37+0.12, 31.06+0.8 and 81.58+ 0.5; Delphinidin (mg/l) 6.34+0.17, 18.98+0.56 and 49.87+0.7; and the appearance of extraction pure anthocyanins in L(a, b): 2.71(1.38, -0.48), 1.06(0.39,-0.66) and 2.64(2.71,-3.61) respectively. Dendrobium Hybrid cv. Ear-Sakul could be used as a source of anthocyanins by simple solvent extraction and stage of flowers as a guideline for the prediction amount of main anthocyanins components are cyanidin, pelargonidin, and delphinidin could be application and development in quantities, and qualities with the advantage for food pharmaceutical and cosmetic industries.

Keywords: analysis, anthocyanins contents, different stage flowers, Dendrobium Hybrid cv. Ear-Sakul

Procedia PDF Downloads 121

Authors: Tetsya Sok, Seong Jae Hong, Young Kyu Kim, Seung Woo Lee

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The temperature distribution for asphalt concrete (AC)-Concrete composite pavement is one of main influencing factor that affects to performance life of pavement. The temperature gradient in concrete slab underneath the AC layer results the critical curling stress and lead to causes de-bonding of AC-Concrete interface. These stresses, when enhanced by repetitive axial loadings, also contribute to the fatigue damage and eventual crack development within the slab. Moreover, the temperature change within concrete slab extremely causes the slab contracts and expands that significantly induces reflective cracking in AC layer. In this paper, the numerical prediction of pavement temperature was investigated using one-dimensional finite different method (FDM) in fully explicit scheme. The numerical predicted model provides a fundamental and clear understanding of heat energy balance including incoming and outgoing thermal energies in addition to dissipated heat in the system. By using the reliable meteorological data for daily air temperature, solar radiation, wind speech and variable pavement surface properties, the predicted pavement temperature profile was validated with the field measured data. Additionally, the effects of AC thickness and daily air temperature on the temperature profile in underlying concrete were also investigated. Based on obtained results, the numerical predicted temperature of AC-Concrete composite pavement using FDM provided a good accuracy compared to field measured data and thicker AC layer significantly insulates the temperature distribution in underlying concrete slab.

Keywords: asphalt concrete, finite different method (FDM), curling effect, heat transfer, solar radiation

Procedia PDF Downloads 247

Authors: Ismail Ay

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In this study, it is aimed to analyze the relations of the factors such as the learned resourcefulness, self-efficacy, self-regulation and subjective well-being which are thought to affect the needs of the university students for psychological help and to determine if the subjective well-being mediates other factors in the prediction of the needs of the university students for psychological help. The population of the study is formed of undergraduates who get education in 16 faculties in the central campus of the University of Atatürk in the spring term of 2012-2013 academic years. The sample of the study is formed of 1205 undergraduates (female=666, 55,3 %; male=539, 44,7 %; average of age =21,49; Sd=2,18) selected from the mentioned universe by convenience sampling method. “Need for Psychological Help Scale” has been developed as a part of the study to determine the needs for psychological help. “Short Self-Regulation Questionnaire” has been adapted into Turkish to determine the self-regulation skills. Apart from these, Rosenbaum’s Learned Resourcefulness Scale, General Self-Efficacy Scale and to determine subjective well-being; Satisfaction with Life Scale and Positive and Negative Affect Scale have been used within the study. SPSS 22.0 and LISREL 9.1 have been used in the analysis of the data. Pearson product-moment correlation, descriptive analysis, factor analysis and path analysis to test the research hypothesis has been used in the study. According to obtained data, the learned resourcefulness factor does not predict the subjective well-being; however, it highly predicts the self-regulation and self-efficacy factors. It has been determined that the self-regulation and self-efficacy factors predict the subjective well-being in a positive way and medium level, and subjective well-being mediates self-regulation and self-efficacy factors to predict the needs for psychological help. It was also determined that subjective well-being predicts the needs for psychological help in a negative way and fair level. All these results have been discussed in terms of the related theories and literature, and several suggestions have been made.

Keywords: need for psychological help, self-regulation, self-efficacy, learned resourcefulness, subjective well-being, Maslow, psychological needs

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Authors: Warda L. Panondi, Norihiro Izumi

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Sedimentation is a significant threat to the sustainability of reservoirs and their watershed. In the Philippines, the Pulangi watershed experienced a high sediment loss mainly due to land conversions and plantations that showed critical erosion rates beyond the tolerable limit of -10 ton/ha/yr in all of its sub-basin. From this event, the prediction of runoff volume and sediment yield is essential to examine using the country's soil conservation techniques realistically. In this research, the Pulangi watershed was modeled using the soil water assessment tool (SWAT) to predict its watershed basin's annual runoff and sediment yield. For the calibration and validation of the model, the SWAT-CUP was utilized. The model was calibrated with monthly discharge data for 1990-1993 and validated for 1994-1997. Simultaneously, the sediment yield was calibrated in 2014 and validated in 2015 because of limited observed datasets. Uncertainty analysis and calculation of efficiency indexes were accomplished through the SUFI-2 algorithm. According to the coefficient of determination (R2), Nash Sutcliffe efficiency (NSE), King-Gupta efficiency (KGE), and PBIAS, the calculation of streamflow indicates a good performance for both calibration and validation periods while the sediment yield resulted in a satisfactory performance for both calibration and validation. Therefore, this study was able to identify the most critical sub-basin and severe needs of soil conservation. Furthermore, this study will provide baseline information to prevent floods and landslides and serve as a useful reference for land-use policies and watershed management and sustainability in the Pulangi watershed.

Keywords: Pulangi watershed, sediment yield, streamflow, SWAT model

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Authors: Hassan Gholibeigian, Kazem Gholibeigian

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In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified.

Keywords: pulsating mantle hypothesis, inner core’s dislocation, outer core’s bulge, constitutive model, transient hydro-magneto-thermo-mechanical load, diurnal stress, jerk, fault behaviour

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Authors: Muhamad Sani Buang, Shahrul Azam Abdullah, Juri Saedon

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There is not much effective guideline on development of design parameters selection on springback for advanced high strength steel sheet metal in U-channel process during cold forming process. This paper presents the development of predictive model for springback in U-channel process on advanced high strength steel sheet employing Response Surface Methodology (RSM). The experimental was performed on dual phase steel sheet, DP590 in U-channel forming process while design of experiment (DoE) approach was used to investigates the effects of four factors namely blank holder force (BHF), clearance (C) and punch travel (Tp) and rolling direction (R) were used as input parameters using two level values by applying Full Factorial design (24). From a statistical analysis of variant (ANOVA), result showed that blank holder force (BHF), clearance (C) and punch travel (Tp) displayed significant effect on springback of flange angle (β2) and wall opening angle (β1), while rolling direction (R) factor is insignificant. The significant parameters are optimized in order to reduce the springback behavior using Central Composite Design (CCD) in RSM and the optimum parameters were determined. A regression model for springback was developed. The effect of individual parameters and their response was also evaluated. The results obtained from optimum model are in agreement with the experimental values

Keywords: advance high strength steel, u-channel process, springback, design of experiment, optimization, response surface methodology (rsm)

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Authors: Nina Penkova, Kalin Krumov, Liliana Zashcova, Ivan Kassabov

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The insulating glass units (IGU) are widely used in the advanced and renovated buildings in order to reduce the energy for heating and cooling. Rules for the choice of IGU to ensure energy efficiency and thermal comfort in the indoor space are well known. The existing of internal loads - gage or vacuum pressure in the hermetized gas space, requires additional attention at the design of the facades. The internal loads appear at variations of the altitude, meteorological pressure and gas temperature according to the same at the process of sealing. The gas temperature depends on the presence of coatings, coating position in the transparent multi-layer system, IGU geometry and space orientation, its fixing on the facades and varies with the climate conditions. An algorithm for modeling and numerical simulation of thermal fields and internal pressure in the gas cavity at insulating glass units as function of the meteorological conditions is developed. It includes models of the radiation heat transfer in solar and infrared wave length, indoor and outdoor convection heat transfer and free convection in the hermetized gas space, assuming the gas as compressible. The algorithm allows prediction of temperature and pressure stratification in the gas domain of the IGU at different fixing system. The models are validated by comparison of the numerical results with experimental data obtained by Hot-box testing. Numerical calculations and estimation of 3D temperature, fluid flow fields, thermal performances and internal loads at IGU in window system are implemented.

Keywords: insulating glass units, thermal loads, internal pressure, CFD analysis

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

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Authors: Eda Orhun

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Purpose: This paper analyzes a target firm’s decision to voluntarily disclose information during a takeover event and the effect of such disclosures on the outcome of the takeover. Such voluntary disclosures especially in the form of earnings forecasts made around takeover events may affect shareholders’ decisions about the target firm’s value and in return takeover result. This study aims to shed light on this question. Design/methodology/approach: The paper tries to understand the role of voluntary disclosures by target firms during a takeover event in the likelihood of takeover success both theoretically and empirically. A game-theoretical model is set up to analyze the voluntary disclosure decision of a target firm to inform the shareholders about its real worth. The empirical implication of model is tested by employing binary outcome models where the disclosure variable is obtained by identifying the target firms in the sample that provide positive news by issuing increasing management earnings forecasts. Findings: The model predicts that a voluntary disclosure of positive information by the target decreases the likelihood that the takeover succeeds. The empirical analysis confirms this prediction by showing that positive earnings forecasts by target firms during takeover events increase the probability of takeover failure. Overall, it is shown that information dissemination through voluntary disclosures by target firms is an important factor affecting takeover outcomes. Originality/Value: This study is the first to the author's knowledge that studies the impact of voluntary disclosures by the target firm during a takeover event on the likelihood of takeover success. The results contribute to information economics, corporate finance and M&As literatures.

Keywords: takeovers, target firm, voluntary disclosures, earnings forecasts, takeover success

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Authors: Talha A. Al-Dubai, Rashad A. Bantan, Ramadan H. Abu-Zied, Brian G. Jones, Aaid G. Al-Zubieri

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Contemporary foraminiferal samples sediments were collected from the intertidal sabkha of Al-Kharrar Lagoon, Saudi Arabia, to study the vertical distribution of Foraminifera and, based on a modern training set, their potential to develop a predictor of former sea-level changes in the area. Based on hierarchical cluster analysis, the intertidal sabkha is divided into three vertical zones (A, B & C) represented by three foraminiferal assemblages, where agglutinated species occupied Zone A and calcareous species occupied the other two zones. In Zone A (high intertidal), Agglutinella compressa, Clavulina angularis and C. multicamerata are dominant species with a minor presence of Peneroplis planatus, Coscinospira hemprichii, Sorites orbiculus, Quinqueloculina lamarckiana, Q. seminula, Ammonia convexa and A. tepida. In contrast, in Zone B (middle intertidal) the most abundant species are P. planatus, C. hemprichii, S. orbiculus, Q. lamarckiana, Q. seminula and Q. laevigata, while Zone C (low intertidal) is characterised by C. hemprichii, Q. costata, S. orbiculus, P. planatus, A. convexa, A. tepida, Spiroloculina communis and S. costigera. A transfer function for sea-level reconstruction was developed using a modern dataset of 75 contemporary sediment samples and 99 species collected from several transects across the sabkha. The model provided an error of 0.12m, suggesting that intertidal foraminifers are able to predict the past sea-level changes with high precision in Al-Kharrar Lagoon, and thus the future prediction of those changes in the area.

Keywords: Lagoonal foraminifers, intertidal sabkha, vertical zonation, transfer function, sea level

Procedia PDF Downloads 147

Authors: Shafait Hussain Ali

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Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

Procedia PDF Downloads 82

Authors: Sikander Nawaz Khan

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Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

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Authors: Ahmad Idris, Bishir Kado, Murtala Umar, Armaya`u Suleiman Labo

Abstract:

Superpave is the short form of Superior Performing Asphalt Pavement and represents a basis for specifying component materials, asphalt mixture design and analysis, and pavement performance prediction. This new technology is the result of long research projects conducted by the strategic Highway Research program (SHRP) of the Federal Highway Administration. This research was aimed at examining the suitability of Aggregates found in Kano for used in Superpave design method. Aggregates samples were collected from different sources in Kano Nigeria and their Engineering properties, as they relate to the SUPERPAVE design requirements were determined. The average result of Coarse Aggregate Angularity in Kano was found to be 87% and 86% of one fractured face and two or more fractured faces respectively with a standard of 80% and 85% respectively. Fine Aggregate Angularity average result was found to be 47% with a requirement of 45% minimum. A flat and elongated particle which was found to be 10% has a maximum criterion of 10%. Sand equivalent was found to be 51% with the criteria of 45% minimum. Strength tests were also carried out, and the results reflect the requirements of the standards. The tests include Impact value test, Aggregate crushing value, and Aggregate Abrasion tests and the results are 27.5%, 26.7%, and 13%, respectively, with the maximum criteria of 30%. Specific gravity was also carried out and the result was found to have an average value of 2.52 with a criterion of 2.6 to 2.9 and Water absorption was found to be 1.41% with maximum criteria of 0.6%. From the study, the result of the tests indicated that the aggregates properties has met the requirements of Superpave design method based on the specifications of ASTMD 5821, ASTM D 4791, AASHTO T176, AASHTO T33 and BS815.

Keywords: Superpave, aggregates, asphalt mix, Kano

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Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

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Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

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Authors: Yeaji Seok, Myung Kyung Lee

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Background: Even if patients survive after a stroke, stroke patients may experience disability in mobility, sensation, cognition, and speech and language. Stroke patients require rehabilitation for functional recovery and daily life for a considerable time. During rehabilitation, the role of caregivers is important. However, the stroke patients’ quality of life may deteriorate due to family caregivers’ non-preparedness and increased role burden. Purpose: To investigate the prediction of caregivers' preparedness and role burden on stroke patients’ quality of life. Methods: The target population was stroke patients who were hospitalized for rehabilitation and their family care providers. A total of 153 patient-family caregiver dyads were recruited from June to August 2021. Data were collected from self-reported questionnaires and analyzed using descriptive statistics, t-tests, chi-squared test, one-way analysis of variance, Pearson’s correlation coefficients, and multiple regression with SPSS statistics 28 programs. Results: Family caregivers’ preparedness affected stroke patients’ mobility (β = .20, p < 0.05) and character (β = -.084, p < 0.05) and production activities (β = -.197, p < 0.05) in quality of life. The role burden of family caregivers affected language skills (β = .310, p<0.05), visual functions (β=-.357, p < 0.05), thinking skills (β = 0.443, p = 0.05), mood conditions (β = 0.565, p < 0.001), family roles (β = -0.361, p < 0.001), and social roles (β = -0.304, p < 0.001), while the caregivers’ burden of performing self-protection negatively affected patients’ social roles (β = .180, p=.048). In addition, caregivers’ role burden of personal life sacrifice affected patients’ mobility (β = .311, p < 0.05), self-care (β =.232, p < 0.05) and energy (β = .239, p < 0.05). Conclusion: This study indicated that family caregivers' preparedness and role burden affected stroke patients’ quality of life. The results of this study suggested that intervention to improve family caregivers’ preparedness and to reduce role burden should be required for quality of life in stroke patients.

Keywords: quality of life, preparedness, role burden, caregivers, stroke

Procedia PDF Downloads 178

Authors: Suwapitch Chalongkulasak, Teerasak E-Kobon, Pramote Chumnanpuen

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Acne vulgaris is a common skin disease mainly caused by the Gram–positive pathogenic bacterium, Propionibacterium acnes. This bacterium stimulates inflammation process in human sebaceous glands. Giant African snail (Achatina fulica) is alien species that rapidly reproduces and seriously damages agricultural products in Thailand. There were several research reports on the medical and pharmaceutical benefits of this snail mucus peptides and proteins. This study aimed to in silico predict multifunctional bioactive peptides from A. fulica mucus peptidome using several bioinformatic tools for determination of antimicrobial (iAMPpred), anti–biofilm (dPABBs), cytotoxic (Toxinpred), cell membrane penetrating (CPPpred) and anti–quorum sensing (QSPpred) peptides. Three candidate peptides with the highest predictive score were selected and re-designed/modified to improve the required activities. Structural and physicochemical properties of six anti–P. acnes (APA) peptide candidates were performed by PEP–FOLD3 program and the five aforementioned tools. All candidates had random coiled structure and were named as APA1–ori, APA2–ori, APA3–ori, APA1–mod, APA2–mod and APA3–mod. To validate the APA activity, these peptide candidates were synthesized and tested against six isolates of P. acnes. The modified APA peptides showed high APA activity on some isolates. Therefore, our biomimetic mucus peptides could be useful for preventing acne vulgaris and further examined on other activities important to medical and pharmaceutical applications.

Keywords: Propionibacterium acnes, Achatina fulica, peptidomes, antibacterial peptides, snail mucus

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Authors: Iftekhar Hassan, Sayedil Morsalin, Easir A Khan

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Accidents involving fire occur frequently in recent times and their causes showing a great deal of variety which require intervention methods and risk assessment strategies are unique in each case. On September 4, 2020, a fire and explosion occurred in a confined space caused by a methane gas leak from an underground pipeline in Baitus Salat Jame mosque during Night (Esha) prayer in Narayanganj District, Bangladesh that killed 34 people. In this research, this incident is simulated using Fire Dynamics Simulator (FDS) software to analyze and understand the nature of the accident and associated consequences. FDS is an advanced computational fluid dynamics (CFD) system of fire-driven fluid flow which solves numerically a large eddy simulation form of the Navier–Stokes’s equations for simulation of the fire and smoke spread and prediction of thermal radiation, toxic substances concentrations and other relevant parameters of fire. This study focuses on understanding the nature of the fire and consequence evaluation due to thermal radiation caused by vapor cloud explosion. An evacuation modeling was constructed to visualize the effect of evacuation time and fractional effective dose (FED) for different types of agents. The results were presented by 3D animation, sliced pictures and graphical representation to understand fire hazards caused by thermal radiation or smoke due to vapor cloud explosion. This study will help to design and develop appropriate respond strategy for preventing similar accidents.

Keywords: consequence modeling, fire and explosion, fire dynamics simulation (FDS), thermal radiation

Procedia PDF Downloads 193

Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Albert Suryaprawira

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The Nasion-Sella Line (NSL) has been used for several years as a reference line in longitudinal growth study. Therefore this line is considered to be stable not only to evaluate treatment outcome and to predict relapse possibility but also to manage prognosis. This is a radiographic superimposition of an adult male aged 19 years who complained of difficulty in aesthetic, talking and chewing. Patient has a midface hypoplasia profile (concave). He was diagnosed to have a severe Skeletal Class III with Class III malocclusion, increased lower vertical height, and an anterior open bite. A pre-treatment cephalometric radiograph was taken to analyse the skeletal problem and to measure the amount of bone movement and the prediction soft tissue response. A panoramic radiograph was also taken to analyse bone quality, bone abnormality, third molar impaction, etc. Before the surgery, a pre-surgical cephalometric radiograph was taken to re-evaluate the plan and to settle the final amount of bone cut. After the surgery, a post-surgical cephalometric radiograph was taken to confirm the result with the plan. The superimposition using NSL as a reference line between those radiographs was performed to analyse the outcome. It is important to describe the amount of hard and soft tissue movement and to predict the possibility of relapse after the surgery. The patient also needs to understand all the surgical plan, outcome and relapse prevention. The surgical management included maxillary impaction and advancement of Le Fort I osteotomy. The evaluation using NSL as a reference was a very useful method in determining the outcome and prognosis.

Keywords: Nasion-Sella Line, midface hypoplasia, Le Fort 1, maxillary advancement

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Authors: Shidvash Vakilipour, Masoud Mohammadi, Rouzbeh Riazi, Scott Ormiston, Kimia Amiri, Sahar Barati

Abstract:

An essential component of a finite volume method (FVM) is the advection scheme that estimates values on the cell faces based on the calculated values on the nodes or cell centers. The most widely used advection schemes are upwind schemes. These schemes have been developed in FVM on different kinds of structured and unstructured grids. In this research, the physical influence scheme (PIS) is developed for a cell-centered FVM that uses an implicit coupled solver. Results are compared with the exponential differencing scheme (EDS) and the skew upwind differencing scheme (SUDS). Accuracy of these schemes is evaluated for a lid-driven cavity flow at Re = 1000, 3200, and 5000 and a backward-facing step flow at Re = 800. Simulations show considerable differences between the results of EDS scheme with benchmarks, especially for the lid-driven cavity flow at high Reynolds numbers. These differences occur due to false diffusion. Comparing SUDS and PIS schemes shows relatively close results for the backward-facing step flow and different results in lid-driven cavity flow. The poor results of SUDS in the lid-driven cavity flow can be related to its lack of sensitivity to the pressure difference between cell face and upwind points, which is critical for the prediction of such vortex dominant flows.

Keywords: cell-centered finite volume method, coupled solver, exponential differencing scheme (EDS), physical influence scheme (PIS), pressure weighted interpolation method (PWIM), skew upwind differencing scheme (SUDS)

Procedia PDF Downloads 250

Authors: Rafael Zhindon Almeida

Abstract:

Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

Procedia PDF Downloads 54

Authors: F. Q. Shao, W. Q. Wang, Y. Yin, T. P. Yu, D. B. Zou, J. M. Ouyang

Abstract:

The Radiation Pressure Acceleration (RPA) mechanism is very promising in laser-driven ion acceleration because of high laser-ion energy conversion efficiency. Although some experiments have shown the characteristics of RPA, the energy of ions is quite limited. The ion energy obtained in experiments is only several MeV/u, which is much lower than theoretical prediction. One possible limiting factor is the transverse instability incited in the RPA process. The transverse instability is basically considered as the Rayleigh-Taylor (RT) instability, which is a kind of interfacial instability and occurs when a light fluid pushes against a heavy fluid. Multi-dimensional particle-in-cell (PIC) simulations show that the onset of transverse instability will destroy the acceleration process and broaden the energy spectrum of fast ions during the RPA dominant ion acceleration processes. The evidence of the RT instability driven by radiation pressure has been observed in a laser-foil interaction experiment in a typical RPA regime, and the dominant scale of RT instability is close to the laser wavelength. The development of transverse instability in the radiation-pressure-acceleration dominant laser-foil interaction is numerically examined by two-dimensional particle-in-cell simulations. When a laser interacts with a foil with modulated surface, the internal instability is quickly incited and it develops. The linear growth and saturation of the transverse instability are observed, and the growth rate is numerically diagnosed. In order to optimize interaction parameters, a method of information entropy is put forward to describe the chaotic degree of the transverse instability. With moderate modulation, the transverse instability shows a low chaotic degree and a quasi-monoenergetic proton beam is produced.

Keywords: information entropy, radiation pressure acceleration, Rayleigh-Taylor instability, transverse instability

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Authors: Pavel A. Strizhak, Geniy V. Kuznetsov, Ivan S. Voytkov, Dmitri V. Antonov

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This paper presents the results of experiments carried out at a specialized test site for establishing macroscopic patterns of heat and mass transfer processes at localizing model combustion sources of ground forest fires with the use of barrier lines in the form of a wetted lay of material in front of the zone of flame burning and thermal decomposition. The experiments were performed using needles, leaves, twigs, and mixtures thereof. The dimensions of the model combustion source and the ranges of heat release correspond well to the real conditions of ground forest fires. The main attention is paid to the complex analysis of the effect of dispersion of water aerosol (concentration and size of droplets) used to form the barrier line. It is shown that effective conditions for localization and subsequent suppression of flame combustion and thermal decomposition of forest fuel can be achieved by creating a group of barrier lines with different wetting width and depth of the material. Relative indicators of the effectiveness of one and combined barrier lines were established, taking into account all the main characteristics of the processes of suppressing burning and thermal decomposition of forest combustible materials. We performed the prediction of the necessary and sufficient parameters of barrier lines (water volume, width, and depth of the wetted lay of the material, specific irrigation density) for combustion sources with different dimensions, corresponding to the real fire extinguishing practice.

Keywords: forest fire, barrier water lines, pyrolysis front, flame front

Procedia PDF Downloads 102

Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

Abstract:

The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

Procedia PDF Downloads 66

Authors: Wenbo Wang, Yi-Fang Brook Wu

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The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

Procedia PDF Downloads 33

Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

Procedia PDF Downloads 32

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

Abstract:

Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 168